Carbon emissions and economic growth in China: Based on mixed frequency VAR analysis

This study endeavors to examine the association between carbon emissions and economic growth in China spanning the period from 1992 to 2018, employing a mixed frequency vector autoregressive model (MF-VAR). In contrast to the low frequency vector autoregressive model (LF-VAR), the MF-VAR model employed in this research does not involve any filtering procedure; instead, it directly employs variables with distinct frequencies for regression computation. Comparative analysis of the findings reveals that the utilization of the MF-VAR model yields bidirectional causality between carbon emissions and economic growth, a relationship that cannot be validated through employment of the LF-VAR model. Furthermore, the prediction error variance decomposition outcomes of the MF-VAR model exhibit a higher level of explicatory capacity when compared to those of the LF-VAR model. These outcomes carry significant implications for China, as well as other global economies, in the pursuit of the Sustainable Development Goals (SDGs). The analysis outcomes further underscore the necessity of effectively curbing greenhouse gas emissions in China, while simultaneously maintaining a certain level of economic development.